[2504.18453] Reason Like a Radiologist: Chain-of-Thought and Reinforcement Learning for Verifiable Report Generation

Computer Science > Artificial Intelligence arXiv:2504.18453 (cs) [Submitted on 25 Apr 2025 (v1), last revised 2 Mar 2026 (this version, v2)] Title:Reason Like a Radiologist: Chain-of-Thought and Reinforcement Learning for Verifiable Report Generation Authors:Peiyuan Jing, Kinhei Lee, Zhenxuan Zhang, Huichi Zhou, Zhengqing Yuan, Zhifan Gao, Lei Zhu, Giorgos Papanastasiou, Yingying Fang, Guang Yang View a PDF of the paper titled Reason Like a Radiologist: Chain-of-Thought and Reinforcement Learning for Verifiable Report Generation, by Peiyuan Jing and 9 other authors View PDF Abstract:Radiology report generation is critical for efficiency but current models lack the structured reasoning of experts, hindering clinical trust and explainability by failing to link visual findings to precise anatomical locations. This paper introduces BoxMed-RL, a groundbreaking unified training framework for generating spatially verifiable and explainable radiology reports. Built on a large vision-language model, BoxMed-RL revolutionizes report generation through two integrated phases: (1) In the Pretraining Phase, we refine the model via medical concept learning, using Chain-of-Thought supervision to internalize the radiologist-like workflow, followed by spatially verifiable reinforcement, which applies reinforcement learning to align medical findings with bounding boxes. (2) In the Downstream Adapter Phase, we freeze the pretrained weights and train a downstream adapter to ensure fluent and cl...